please answer CG Q0A - CG Q5B
\# CG Q2b \# Use the summary() function on logisticFit \#\#\#\#\# to access the results of the regression. \# CG Q2C \# Print the coefficient for temperature. Use coef(logisticFit) \#\#\#\#\#\# followed by the name of that coefficient in quotes \#\#\#\#\#\# inside square brackets as you did in your linear regression HW. \# CG Q2d \# Now print the multiplicative effect of temperature \#\#\#\#\#\# on the odds of ride count being higher than 7,000 by \#\#\#\#\#\# wrapping the line of code from Q2c in the exp() function. \# CG Q2e \# Using the result from Q2d, determine how the odds of the \#\#\#\#\#\#\# ride count being higher than 7,000 change with a 1 degree \#\#\#\#\#\# increase in temperature. 34 \#\#\#\#\#\#\# A: up 1 deqree, B: up 13 deqrees, C: up bv 1%, D: up 13%, E: down 12% \#\#\#\#\#\#\# Use paste("letter") to indicate your answer. For example, \#\#\#\#\#\#\# if you think A is the correct answer, type paste("A"). \# CG Q2f \# Find the R-squared for the regression. \#\#\#\#\#\#\# In your calculation, use logisticFit\$deviance and \#\#\#\#\#\# logisticFit\$null. deviance and not the numbers printed \#\#\#\#\#\# in the summary output for these. \# Question 3 \# Predict probability of success \# CG Q3a \# You will predict the probability of more than 7,000 rides \#\#\#\#\#\# when it's 25 degrees celsius day. \#\#\#\#\#\#\# Create a data frame defining this value and name it newdata. \# CG Q3b \# Use the predict() function to predict the probability of \#\#\#\#\#\#\# demand for more than 7,000 rides using newdata from Q3a. \# Question 4 \# a logistic regression with multiple predictors \# CG Q4a \# Build a logistic regression model that has high ride count \#\#\#\#\#\# as the response modeled by the weather variables \#\#\#\#\#\# weathersit, temp, hum, and windspeed. \#\#\#\#\#\# Use an additive model (don't model interactions or anything fancy). \#\#\#\#\#\#\# Name your fitted model logistic2. \# CG Q4b \# Use the summary() function on logistic2 \#\#\#\#\#\# to access the results of the regression. \# CG Q4C \# Print the coefficient for windspeed. Use coef(logistic2) \#\#\#\#\#\# followed by the name of that coefficient in quotes \#\#\#\#\#\#\# inside square brackets as you did in your linear regression HW. \# CG Q4d \# Now print the multiplicative effect of temperature \#\#\#\#\#\# on the odds of ride count being higher than 7,000 by \#\#\#\#\#\# wrapping the line of code from Q2c in the exp() function. \# CG Q4e \# Using the result from Q4d, determine how the odds of the \#\#\#\#\#\# ride count being higher than 7,000 change with a 1mph \#\#\#\#\#\# increase in windspeed (holding other predictors constant). \#\#\#\#\#\# A: down 0.1mph, B: up 0.9mph, C: up by 1%,D : up 90%,E : down 10\% \#\#\#\#\#\# Use paste("letter") to indicate your answer. For example, \#\#\#\#\#\# if you think A is the correct answer, type paste("A"). \# CG Q4f \# Find the R-squared for the regression. \#\#\#\#\#\# In your calculation, use logistic2\$deviance and \#\#\#\#\#\# logistic2\$null. deviance and not the numbers printed \#\#\#\#\#\# in the summary output for these. \# Question 5 \# Predict probability of success \# CG Q5a \# You will predict the probability of more than 7,000 rides \#\#\#\#\#\# on a clear, 25 degrees celsius day (77 degrees farenheit) \#\#\#\#\#\# with 50% humidity and windspeed 5. \#\#\#\#\#\# Create a data frame defining these values and name it newdata2. \# CG Q5b \# Use the predict() function to predict the probability of \#\#\#\#\#\# demand for more than 7,000 rides using newdata2 from Q5a
